Big Tech’s strategy for selling AI: Dogfooding

Meta’s Mark Zuckerberg wants you to know he’s building an AI agent to help him be CEO — and that eventually everyone should have one. Jensen Huang is broadcasting that he’d be “deeply alarmed” if Nvidia’s $500,000 engineers weren’t burning through $250,000 in AI tokens a year. Salesforce keeps talking about “digital labor” like it’s already a line item in your budget.

You can take all of this at face value. Or you can recognize a familiar move: the people selling the future are making a point of telling you they’re living in it first. It’s a little like Hair Club for Men. They’re not just pitching the product — they’re the testimonial.

But what’s interesting isn’t just the marketing; it’s how closely the messaging aligns with their business interests and the billions they’ve already poured in.

Across Big Tech, CEOs are starting to define what “good” looks like in an AI world. At Meta, that means flattening teams and pushing employees to use internal AI tools so aggressively that it shows up in performance reviews. At Nvidia, it means tying productivity to token consumption: if you’re not spending enough on AI, something’s wrong. At Salesforce, CEO Marc Benioff tells anyone who will listen that companies will soon manage fleets of “digital workers” alongside humans.

Of course, tech has a long history of “dogfooding,” or using its own products internally to make them better. But this feels different. AI is still poorly understood by most of the people being asked to buy it, and at the same time they’re being told it’s inevitable. They’re pushing internal adoption, and then pointing to that adoption as proof it works.

To be clear, this doesn’t mean they’re wrong. The uncomfortable part is that they might be early and self-interested at the same time. AI probably can make individuals more productive. Agents probably will change how work gets done. Compute probably will become a core input, like cloud spend before it.

Overall business spending on AI has been growing, and the size of those contracts has been growing as well, according to data from Ramp, a corporate card and expense management platform, suggesting that companies are finding them useful.

“ Companies are not irrational actors that are spending money with no return on investment,” Ara Kharazian, Ramp’s economist, told Sherwood News. “When they’re buying these sort of verticalized specific software solutions, it’s because they’re expanding their contracts and seats in order to capture more gains.”

But so far, the external data showing AI productivity is limited. Federal Reserve Bank of St. Louis Real-Time Population Survey data shows that while about 40% of adults use AI at work, the time saved amounts to only about 2% of total work hours. A survey of 1,000 hiring managers by Resume.org found that AI’s impact on jobs has been minimal so far, with 9% saying it had fully replaced certain roles and 45% saying it had little to no impact on staffing. In one of the first large real-world studies, researchers found that AI boosted productivity among customer service workers by about 15% on average — though gains were uneven and concentrated among less experienced employees.

In the absence of robust proof, marketing fills the gap.

This works because companies don’t just buy software — they copy norms. If the CEO of Nvidia says serious engineers should be using massive amounts of compute, that doesn’t stay contained to Nvidia. It seeps into how other companies evaluate their own teams. If Zuckerberg says the future org chart is flatter and agent-driven, that becomes less of a Meta experiment and more of a managerial benchmark.

Nvidia benefits from a world where “good” engineers use a lot of tokens. Salesforce benefits from a world where every company believes it needs AI “employees.” Meta benefits from a world where agents are everywhere — and always running up the tab. In each case, the definition of competence conveniently expands demand for the thing they sell.

There’s also a simpler explanation for the urgency: AI is really, really expensive. The biggest tech companies are collectively spending hundreds of billions of dollars on data centers, chips, and power. That kind of fixed cost only works if usage keeps climbing. So the message shifts from, “This might help,” to, “You should be doing a lot more of this already.” The faster AI becomes table stakes, the faster those investments start to look justified.

And it’s a very effective way to sell a very expensive product.